A three-tier AI solution for equitable glaucoma diagnosis across China’s hierarchical healthcare system

作者：Yi Zhou, Haitao Nie, Xinyu Gong, Minhui Dai, Zhaohong Guo, Xiaoling Deng, Mengyang Li, Yong Liu, Lingyu Sun, Xiangyi Tang, Ling Zhou, Zhiyao Tang, Zi-Qing Xia, Lemeng Feng, Wulong Zhang, Qingqing Yi, Xiaobo Xia, Bin Xie, Weitao Song · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-025-01835-4 · 被引用次数：7 · 研究领域：Retinal Imaging and Analysis、Glaucoma and retinal disorders、Acute Ischemic Stroke Management

Artificial intelligence (AI) offers a solution to glaucoma care inequities driven by uneven resource distribution, but its real-world implementation remains limited. Here, we introduce Multi-Glau, an three-tier AI system tailored to China's hierarchical healthcare system to promote health equity in glaucoma care, even in settings with limited equipment. The system comprises three modules: (1) a screening module for primary hospitals that eliminates reliance on imaging; (2) a pre-diagnosis module for handling incomplete data in secondary hospitals, and (3) a definitive diagnosis module for the precise diagnosis of glaucoma severity in tertiary hospitals. Multi-Glau achieved high performance (AUC: 0.9254 for screening, 0.8650 for pre-diagnosis, and 0.9516 for definitive diagnosis), with its generalizability confirmed through multicenter validation. Multi-Glau outperformed state-of-the-art models, particularly in handling missing data and providing precise glaucoma severity diagnosis, while improving ophthalmologists' performance. These results demonstrate Multi-Glau's potential to bridge diagnostic gaps across hospital tiers and enhance equitable healthcare access.